scholarly journals Estudo da Temperatura da Superfície do Mar para os Oceanos Atlântico e Pacífico Utilizando a Técnica de Análises de Componente Principal e de Agrupamento (Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of...)

2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Research Laboratory ◽  
Principal Component ◽  
Component Analysis ◽  
Sea Surface ◽  
Earth System ◽  
System Research

Apresenta-se neste estudo a avaliação da metodologia de Análises Multivariadas: Análises em Componente Principal (ACP) e de Agrupamento (AA), aos dados de Temperatura da Superfície do Mar (TSM) para os Oceanos Atlântico (Norte (NATL), Tropical (TROP) e Sul (SATL)) e Pacifico (NIÑO1+2, NIÑO3.4, NIÑO3 e NIÑO4). Foram utilizados dados mensais de janeiro de 1950 a dezembro de 2010 de TSM obtidos na NOAA (National Oceanic and Atmospheric Administration/Earth System Research Laboratory). As regiões TROP e NIÑO4 apresentam as maiores TSM para os meses entre dezembro-julho. A região NATL apresenta no período de agosto-outubro seu maiores valores de TSM. A região NIÑo1+2 apresentou os menores valores de TSM. Os resultados da Análise em Componente Principal (ACP) identificaram maiores pesos na variação total explicada pelas duas primeiras componentes, que representam cerca de 100% da variância total dos dados de TSM. A Análise de Agrupamento (AA), pelo método Ward, permitiu o agrupamento das estações em três grupos homogêneos. Palavras - chave: Análises Multivariadas, Mudanças climáticas, Aquecimento Global.   Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of Principal Component Analysis and Cluster   ABSTRACT Presented in this study was to evaluate the methodology of Multivariate Analysis: Principal Component Analysis (PCA) and cluster analysis (CA), the data of sea surface temperature (SST) for the Atlantic (North (NATL), Tropical (TROP) and South (Satler)) and Pacific (+2 NIÑO1, NIÑO3.4, and NIÑO3 NIÑO4). We used monthly data from January 1950 to December 2010 SST obtained from NOAA (National Oceanic and Atmospheric Administration / Earth System Research Laboratory). TROP and NIÑO4 regions have the highest SST for the months from December to July. NATL The region has in the period August-October SST your highest values +2 NIÑo1 The region had the lowest values of TSM. Results on Principal Component Analysis (PCA) identified higher weights in the total variation explained by the first two components, which represent about 100% of the total variance of SST. The Cluster Analysis (AA), the Ward method, allowed the grouping of stations into three homogeneous groups. Keywords: Multivariate Analysis, Climate Change, Global Warming.

scholarly journals Sea surface temperature patterns in Tropical Atlantic: principal component analysis and nonlinear principal component analysis

2014 ◽  
Vol 1 (1) ◽  
pp. 235-267 ◽  
Author(s):  
S. C. Kenfack ◽  
K. F. Mkankam ◽  
G. Alory ◽  
Y. du Penhoat ◽  
N. M. Hounkonnou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Principal Component ◽  
Component Analysis ◽  
Sea Surface ◽  
Tropical Atlantic ◽  
Composite Analysis ◽  
Main Mode ◽  
Nonlinear Principal Component Analysis

Abstract. Principal Component Analysis (PCA) is one of the popular statistical methods for feature extraction. The neural network model has been performed on the PCA to obtain nonlinear principal component analysis (NLPCA), which allows the extraction of nonlinear features in the dataset missed by the PCA. NLPCA is applied to the monthly Sea Surface Temperature (SST) data from the eastern tropical Atlantic Ocean (29° W–21° E, 25° S–7° N) for the period 1982–2005. The focus is on the differences between SST inter-annual variability patterns; either extracted through traditional PCA or the NLPCA methods.The first mode of NLPCA explains 45.5% of the total variance of SST anomaly compared to 42% explained by the first PCA. Results from previous studies that detected the Atlantic cold tongue (ACT) as the main mode are confirmed. It is observed that the maximum signal in the Gulf of Guinea (GOG) is located along coastal Angola. In agreement with composite analysis, NLPCA exhibits two types of ACT, referred to as weak and strong Atlantic cold tongues. These two events are not totally symmetrical. NLPCA thus explains the results given by both PCA and composite analysis. A particular area observed along the northern boundary between 13 and 5° W vanishes in the strong ACT case and reaches maximum extension to the west in the weak ACT case. It is also observed that the original SST data correlates well with NLPCA and PCA, but with a stronger correlation on ACT area for NLPCA and southwest in the case of PCA.

PRINCIPAL COMPONENT ANALYSIS OF SEA SURFACE TEMPERATURE IN THE NORTH ATLANTIC OCEAN

2009 ◽  
Vol 20 (11) ◽  
pp. 1789-1802 ◽  
Author(s):  
CONSTANTIN ANDRONACHE
Keyword(s):  
Principal Component Analysis ◽  
Atlantic Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Principal Component ◽  
Sea Surface ◽  
The North ◽  
The North Atlantic

The framework of principal component analysis (PCA) based on singular value decomposition (SVD) is applied to the monthly sea surface temperature (SST) observations in the North Atlantic Ocean for the time interval 1856–2008. Multiyear time series of SST for each month are used to investigate the statistical relationship between SST variations from the 12 months. To obtain approximate stationary conditions, the trend and a multidecadal oscillation are removed from the data. The remaining SST residuals exhibit remarkable correlation between successive months, due largely to persistence. PCA demonstrates the dimension reduction of the data sets and provides a robust way of analyzing multivariate observations describing the climate system.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

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